Minimizing the setup costs caused by color changes is one of the main concerns for paint shop scheduling in the automotive industry. Yet, finding an optimized color sequence is a very challenging task, as a large number of exterior systems for car manufacturing need to be painted in a variety of different colors. Therefore, there is a strong need for efficient automated scheduling solutions in this area. Previously, exact and metaheuristic approaches for creating efficient paint shop schedules in the automotive supply industry have been proposed and evaluated on a publicly available set of real-life benchmark instances. However, optimal solutions are still unknown for many of the benchmark instances, and there is still a potential of reducing color change costs for large instances. In this paper, we propose a novel large neighborhood search approach for the paint shop scheduling problem. We introduce innovative exact and heuristic solution methods that are utilized within the large neighborhood search and show that our approach leads to improved results for large real-life problem instances compared to existing techniques. Furthermore, we provide previously unknown upper bounds for 14 benchmark instances using the proposed method.

最大限度地减少由颜色变化引起的设置成本是汽车行业喷漆车间调度的主要问题之一。然而，找到优化的颜色序列是一项非常具有挑战性的任务，因为汽车制造的大量外部系统需要涂上各种不同的颜色。因此，该领域迫切需要高效的自动化调度解决方案。以前，已经提出并评估了用于在汽车供应行业中创建高效涂装车间时间表的精确和元启发式方法，并在一组公开的现实生活基准实例上进行了评估。然而，对于许多基准实例来说，最佳解决方案仍然未知，并且对于大型实例来说，仍然有降低颜色变化成本的潜力。在本文中，我们为油漆车间调度问题提出了一种新颖的大型邻域搜索方法。我们引入了在大型邻域搜索中使用的创新精确和启发式解决方案方法，并表明与现有技术相比，我们的方法可以改善大型现实生活问题实例的结果。此外，我们使用所提出的方法为 14 个基准实​​例提供了以前未知的上限。